On generating a hierarchy for GSPN analysis

ABSTRACT

This paper examines the (semi) automatic generation of a hierarchical structure for generalized stochastic Petri nets (GSPNs). The idea is to partition a GSPN automatically into a set of components with asynchronous communication. Net level results obtained by invariant computation for these subnets are used to define a macro description of the internal state. This yields a hierarchical structure which is exploited in several efficient analysis algorithms. These algorithms include reachability set/graph generation, structured numerical analysis techniques and approximation techniques based on decomposition and aggregation. A GSPN model of an existing production cell and its digital control is analyzed to demonstrate usefulness of the approach.

INDEX TERMS

Primary Classification:
  D. Software
  D.2 SOFTWARE ENGINEERING
      D.2.2 Design Tools and Techniques
          Subjects: Petri nets

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Markov processes; Stochastic processes

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING

General Terms:
Design, Measurement, Performance, Theory

Keywords:
Kronecker algebra, Markov chain analysis techniques, generalized stochastic Petri nets, hierarchical structure